DOI: https://doi.org/10.1093/pnasnexus/pgad207
Acoustic Needles: 3D Microfluidics using Focused Ultrasound passing through Hydrophobic Meshes
DOI:
https://doi.org/10.51094/jxiv.166Keywords:
Microfluidics, Acoustic Radiation Force, Hydrophobic, AutomationAbstract
Current experiments in chemistry, biology, medicine, and engineering require the manipulation of multiple chemicals, samples, and specimens on a large scale. Therefore, automation techniques for manipulating microliter droplets are essential to improve the throughput, reproducibility, and sustainability of experiments. Digital microfluidic methods, such as EWOD (electrowetting-on-dielectric), electrostatics, and acoustophoretic platforms, offer excellent maneuverability and fast control for droplets. However, they are limited in terms of three-dimensional (3D) manipulation and droplet size. Here, we propose an acoustic needles platform, a 3D digital microfluidics system based on focused ultrasound waves (3D-MFUS) that pass through a hydrophobic mesh with droplets resting on it. A focused beam (acoustic needle), generated dynamically by a phased array, creates a stable trap through the mesh and attracts droplets to its focus. This needle can be steered to translate droplets on the surface; droplets can be manipulated simultaneously by generating multiple foci. Moreover, a liquid droplet can be detached from the surface and propelled into mid-air for up to 10.9 cm. This height is 27 and 2 times greater than that observed in the state-of-the-art methods in EWOD and photovoltaics, respectively. Droplets can be merged or split by pushing them against a hydrophobic knife. Additionally, both solid particles and liquid droplets can be manipulated using the same system. This platform would allow scientists and engineers to manipulate liquid droplets in a 3D circuit; moreover, it paves the way for developments in micro-robotics, additive manufacturing, and laboratory automation research.
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Submitted: 2022-09-13 00:24:22 UTC
Published: 2022-09-15 08:53:23 UTC
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Copyright (c) 2022
Yusuke Koroyasu
Thanh-Vinh Nguyen
Shun Sasaguri
Asier Marzo
Iñigo Ezcurdia
Yuuya Nagata
Takayuki Hoshi
Yoichi Ochiai
Tatsuki Fushimi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
